Abstract
BackgroundModelling risk of malaria in longitudinal studies is common, because individuals are at risk for repeated infections over time. Malaria infections result in acquired immunity to clinical malaria disease. Prospective cohorts are an ideal design to relate the historical exposure to infection and development of clinical malaria over time, and analysis methods should consider the longitudinal nature of the data. Models must take into account the acquisition of immunity to disease that increases with each infection and the heterogeneous exposure to bites from infected Anopheles mosquitoes. Methods that fail to capture these important factors in malaria risk will not accurately model risk of malaria infection or disease.MethodsStatistical methods applied to prospective cohort studies of clinical malaria or Plasmodium falciparum infection and disease were reviewed to assess trends in usage of the appropriate statistical methods. The study was designed to test the hypothesis that studies often fail to use appropriate statistical methods but that this would improve with the recent increase in accessibility to and expertise in longitudinal data analysis.ResultsOf 197 articles reviewed, the most commonly reported methods included contingency tables which comprised Pearson Chi-square, Fisher exact and McNemar’s tests (n = 102, 51.8%), Student’s t-tests (n = 82, 41.6%), followed by Cox models (n = 62, 31.5%) and Kaplan–Meier estimators (n = 59, 30.0%). The longitudinal analysis methods generalized estimating equations and mixed-effects models were reported in 41 (20.8%) and 24 (12.2%) articles, respectively, and increased in use over time. A positive trend in choice of more appropriate analytical methods was identified over time.ConclusionsDespite similar study designs across the reports, the statistical methods varied substantially and often represented overly simplistic models of risk. The results underscore the need for more effort to be channelled towards adopting standardized longitudinal methods to analyse prospective cohort studies of malaria infection and disease.
Highlights
Modelling risk of malaria in longitudinal studies is common, because individuals are at risk for repeated infections over time
Well-designed prospective longitudinal cohort studies can be key to understanding risk of malaria disease for participants over time, but appropriate analysis of the collected data is critical for accurate results
Inclusion and exclusion criteria Original research articles were included in the analysis if they were prospective cohort studies where participants were actively or passively followed up for repeated malaria episodes over time and described the following outcomes: clinical malaria based on clinical symptoms and confirmed by rapid diagnostic test (RDT) or microscopy, infection defined as polymerase chain reaction (PCR), microscopy, or RDT results positive for Plasmodium parasites
Summary
Modelling risk of malaria in longitudinal studies is common, because individuals are at risk for repeated infections over time. Well-designed prospective longitudinal cohort studies can be key to understanding risk of malaria disease for participants over time, but appropriate analysis of the collected data is critical for accurate results. Malaria cohorts are characterized by repeated measurements, resulting in possible correlated infection and disease episode data from same participant. Appropriate longitudinal data analysis techniques should account for such possible within-participant correlation and different covariance structures of episodes of malaria or infection measurements over time. These include generalized estimating equations (GEE) and mixed-effects models. Recurrent time-to-event models are the appropriate techniques for analysing recurrent malaria episode data because they take into account the within-participant non-independence of episodes. These techniques include frailty models [20, 21] or recurrent Cox-extended models, such as the Andersen–Gill and Prentice–Williams–Peterson models [17]
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